Why not string theory? Because enough is enough • Hacker News Board

Re: Why not string theory? Because enough is enough

#31

Post by SiVal » Sun, Jun 12, 2016, 6:55 AM UTC

colordrops wrote: post #16

The author says that he "became more convinced [string theorists] are merely building a mathematical toy universe." The explanation for his conclusion was that they kept revising string theory in order to fit observations. I know little of string theory, so I am probably misreading his meaning, but isn't that how all science works? Create a model, then revise it when you get more data.

Well, nothing describes how "all science works", but ideally you don't revise your model based on new data. Instead, you take all of the data you have, both old and new, start over from scratch and try to find the simplest model that would account for it. What your old favorite model used to be, before you obtained the new data, should not play any role in your evaluation of competing models.

Given how real people and organizations work, psychology, resource limitations, incentives, etc., this is VERY idealistic, but still, imagine that you had found your data in a different order, so you ended up with the same data you have now, but had a different subset previously. With two different subsets in the past, your best past models might have been different from each other, but your best choice now should be the same now that both paths have converged on the same data. So why should your choice in the past have a "vote" in your choice now?

So (again, ideally), don't "revise" your old model; take the data you have now, old and new, pretend you are starting from scratch, and choose the best model. If a theory such as String Theory keeps failing to account for new data and is repeatedly modified to keep it from being disqualified, a reasonable question would be whether, if we started from scratch knowing what we know now, we would come up with this repeatedly patched String Theory version as our first choice.

(And, yes, I know that from a Bayesian perspective you can't literally start "from scratch", but that doesn't mean you have to use your most recent model as your prior).

Re: Why not string theory? Because enough is enough

#32

Post by lalalandland » Sun, Jun 12, 2016, 6:58 AM UTC

spectrum1234 wrote: post #22

This ties in with my thinking that there is just too much funding to do research for the sake of research. Let the private sector work on moonshots if they want but more realistically no one should be researching super far out problems. Instead the agile "just in time" approach needs to be used in academia as well as private companies. A good analogy is no one was trying to build electric cars fifty years ago but now…

I think you are throwing the baby out with the bath water here. Science is a cornerstone of the society we live in. To actively explore reality to better understand it is one of humanities greatest achievements.

Flamebait: Should we stop funding schools because most kids are bad at reading ?

Re: Why not string theory? Because enough is enough

#33

Post by venomsnake » Sun, Jun 12, 2016, 6:59 AM UTC

colordrops wrote: post #16

The author says that he "became more convinced [string theorists] are merely building a mathematical toy universe." The explanation for his conclusion was that they kept revising string theory in order to fit observations. I know little of string theory, so I am probably misreading his meaning, but isn't that how all science works? Create a model, then revise it when you get more data.

Yes. But you must generally increase the predictive power of the theory for it to makes sense - otherwise you are creating a religion or mythos. After all - that was how the ancients explained the world - this rock is here because Hercules was mad and threw it all the way from the Danube.

Re: Why not string theory? Because enough is enough

#34

Post by sidek » Sun, Jun 12, 2016, 7:04 AM UTC

visarga wrote: post #30

This is probably a naive question: can we input a bunch of particle interactions into a deep learning system and train it to predict the probability of future interactions? It would be like a "black box version" of physics. If we can predict, then we can find a more elegant mathematical notation and a more intuitive physical interpretation. Machine learning can observe and learn patterns that are more complex than hu…

So, how many bits are you giving the deep learning machine? If you give it enough bits, you aren't predicting anything -- you're just feeding it experimental data which it more or less spits back out.

For an elegant solution, one would need to have deep learning that also optimises on a constrained set of bits.

I feel like in this scenario, humans perform much better. The main strength of computer learning is being able to harness a massive dataset and really good storage capabilities.

Also, you would need to impose a "consistency" constraint on the computer which would be hard to do. Like a computer might say if (mass > 5) do this; else do that. And that is valid computationally. We do that in our split between gr/qm. But in some sense we feel this is wrong physically. The universe shouldn't run on arbitrary if statements.

So I think the answer is just no: the computer algorithms we have today can't handle the problem's constraints.

Re: Why not string theory? Because enough is enough

#35

Post by ProcessBlue » Sun, Jun 12, 2016, 7:09 AM UTC

visarga wrote: post #30

This is probably a naive question: can we input a bunch of particle interactions into a deep learning system and train it to predict the probability of future interactions? It would be like a "black box version" of physics. If we can predict, then we can find a more elegant mathematical notation and a more intuitive physical interpretation. Machine learning can observe and learn patterns that are more complex than hu…

Short answer no.

Long answer, the input you are thinking of (particle interactions) is already what partly defines the theory you are trying to uncover. So you have a serious chicken-egg situation.

You could think about inputting the data that comes out of particle detectors to look for patterns etc. and that sort of thing is being done already. However there is a difference between finding existing patterns and then figuring out the underlying theory which is the mathematical construct that lets you predict things.

Finally, even if you could have a "black box" predict stuff (correctly), the black box-ness is a serious problem for scientists from a meta-science or philosophy of science point of view, and people would be highly unsatisfied until they actually understood what was going on.

Re: Why not string theory? Because enough is enough

#36

Post by archeopterix » Sun, Jun 12, 2016, 7:22 AM UTC

MawNicker wrote: post #27

spectrum1234 wrote: post #22

This ties in with my thinking that there is just too much funding to do research for the sake of research. Let the private sector work on moonshots if they want but more realistically no one should be researching super far out problems. Instead the agile "just in time" approach needs to be used in academia as well as private companies. A good analogy is no one was trying to build electric cars fifty years ago but now…

Babbage designed a computer almost 200 years ago. We need people to be working on moonshots. A lot of them will fail. When one finally succeeds it pierces the veil of "just in time" capitalistic motivation. The financial incentive is certainly perverse in this case. Everyone sells their ideas up front in a frenzied competition to exist . We need a lot more research for the sake of research. More varied and more usele…

But I don't want moonshots, I want moon transit.

Re: Why not string theory? Because enough is enough

#37

Post by seanmcdirmid » Sun, Jun 12, 2016, 7:28 AM UTC

visarga wrote: post #30

This is probably a naive question: can we input a bunch of particle interactions into a deep learning system and train it to predict the probability of future interactions? It would be like a "black box version" of physics. If we can predict, then we can find a more elegant mathematical notation and a more intuitive physical interpretation. Machine learning can observe and learn patterns that are more complex than hu…

The idea that the universe runs on a simple program with simple rules hasn't failed us so far. All we have to do is figure out what that simple program is with our capability for abstract symbolic thinking and reasoning; machines are presently very bad at this while humans are less bad at it.

Re: Why not string theory? Because enough is enough

#38

Post by ddebernardy » Sun, Jun 12, 2016, 7:29 AM UTC

colordrops wrote: post #16

The author says that he "became more convinced [string theorists] are merely building a mathematical toy universe." The explanation for his conclusion was that they kept revising string theory in order to fit observations. I know little of string theory, so I am probably misreading his meaning, but isn't that how all science works? Create a model, then revise it when you get more data.

Think flat world, and the plethora of adjustments that astronomers introduced to it when observations conflicted with theory. As some point you have to throw the towel and come up with a completely different explanation of whatever you're observing.

Re: Why not string theory? Because enough is enough

#39

Post by visarga » Sun, Jun 12, 2016, 7:43 AM UTC

sidek wrote: post #34

visarga wrote: post #30

This is probably a naive question: can we input a bunch of particle interactions into a deep learning system and train it to predict the probability of future interactions? It would be like a "black box version" of physics. If we can predict, then we can find a more elegant mathematical notation and a more intuitive physical interpretation. Machine learning can observe and learn patterns that are more complex than hu…

So, how many bits are you giving the deep learning machine? If you give it enough bits, you aren't predicting anything -- you're just feeding it experimental data which it more or less spits back out. For an elegant solution, one would need to have deep learning that also optimises on a constrained set of bits. I feel like in this scenario, humans perform much better. The main strength of computer learning is being a…

Enough bits to make it efficient at predicting. When it predicts better than the current theory, it starts getting closer to having enough bits. That's the beauty of ML, you don't need to worry about these details if it gives good accuracy.

My intuition was that there could be different ways to explain the laws of Physics that don't look like the current ones which evolved based on human intuition, math and language ability. A non-anthropocentric Physics if you will.

Re: Why not string theory? Because enough is enough

#40

Post by basicplus2 » Sun, Jun 12, 2016, 7:48 AM UTC

visarga wrote: post #30

This is probably a naive question: can we input a bunch of particle interactions into a deep learning system and train it to predict the probability of future interactions? It would be like a "black box version" of physics. If we can predict, then we can find a more elegant mathematical notation and a more intuitive physical interpretation. Machine learning can observe and learn patterns that are more complex than hu…

possibly but the answer might be 42